Accuracy of the Gothenburg congestion charges forecast

This paper explores the accuracy of the transport model forecast of the Gothenburg congestion charges, implemented in 2013. The design of the charging system implies that the path disutility cannot be computed as a sum of link attributes. The route choice model is therefore implemented as a hierarchical algorithm, applying a continuous value of travel time (VTT) distribution. The VTT distribution was estimated from stated choice (SC) data. However, based on experience of impact forecasting with a similar model and of impact outcome of congestion charges in Stockholm, the estimated VTT distribution had to be stretched to the right. We find that the forecast traffic reductions across the cordon and travel time gains were close to those observed in the peak. However, the reduction in traffic across the cordon was underpredicted off-peak. The necessity to make the adjustment indicates that the VTT inferred from SC data does not reveal the travellers’ preferences, or that there are factors determining route choice other than those included in the model: travel distance, travel time and congestion charge.     

New insights on random regret minimization models

This paper develops new methodological insights on Random Regret Minimization (RRM) models. It starts by showing that the classical RRM model is not scale-invariant, and that – as a result – the degree of regret minimization behavior imposed by the classical RRM model depends crucially on the sizes of the estimated taste parameters in combination with the distribution of attribute-values in the data. Motivated by this insight, this paper makes three methodological contributions: (1) it clarifies how the estimated taste parameters and the decision rule are related to one another; (2) it introduces the notion of “profundity of regret”, and presents a formal measure of this concept; and (3) it proposes two new family members of random regret minimization models: the μRRM model, and the Pure-RRM model. These new methodological insights are illustrated by re-analyzing 10 datasets which have been used to compare linear-additive RUM and classical RRM models in recently published papers. Our re-analyses reveal that the degree of regret minimizing behavior imposed by the classical RRM model is generally very limited. This insight explains the small differences in model fit that have previously been reported in the literature between the classical RRM model and the linear-additive RUM model. Furthermore, we find that on 4 out of 10 datasets the μRRM model improves model fit very substantially as compared to the RUM and the classical RRM model.     

A stochastic fleet size and mix model for maintenance operations at offshore wind farms

A fleet of vessels and helicopters is needed to support maintenance operations at offshore wind farms. The cost of this fleet constitutes a major part of the total maintenance costs, hence keeping an optimal or near-optimal fleet is essential to reduce the cost of energy. In this paper we study the vessel fleet size and mix problem that arises for the maintenance operations at offshore wind farms, and propose a stochastic three-stage programming model. The stochastic model considers uncertainty in vessel spot rates, weather conditions, electricity prices and failures to the system. The model is tested on realistic-sized problem instances, and the results show that it is valuable to consider uncertainty and that the proposed model can be used to solve instances of a realistic size.     

The Northern Sea Route competitiveness for oil tankers

This article proposes a decision model for a ship-owner who contemplates the benefits of sailing north via the Northern Sea Route (NSR) or south via the Suez Canal Route (SCR) when transporting oil products from Russia to Asia. The decision is based on potential cost and transit time savings that change on a monthly basis according to sailing conditions and the area along the NSR. This study is applied to a 1A Ice-Class Panamax tanker vessel sailing through the NSR compared to a Panamax tanker vessel sailing through the SCR. It concludes that the NSR provides a competitive advantage in the months from August to November when conservative assumptions on ice conditions (higher bound) are considered for the level of ice thickness encountered along the route and from July to November when a lower bound is assumed.     

An efficient algorithm for smoothing airspace congestion by fine-tuning take-off times

Current technological advances in communications and navigation have improved air traffic management (ATM) with new decision support tools to balance airspace capacity with user demands. Despite agreements achieved in flying reference business trajectories (RBTs) among different stakeholders, tight spatio-temporal connectivity between trajectories in dense sectors can cause perturbations that might introduce time or space deviations into the original RBTs, thus potentially affecting other 4D trajectories. In this paper, several challenging results are presented by properly tuning the Calculated Take-Off Times (CTOTs) as a tool for mitigating the propagation of perturbations between trajectories that can readily appear in dense sectors. Based on the identification of “collective microregions”, a tool for predicting potential spatio-temporal concurrence events between trajectories over the European airspace was developed, together with a CTOT algorithm to sequence the departures that preserve the scheduled slots while relaxing tight trajectory interactions. The algorithm was tested by considering a realistic scenario (designed and analyzed in the STREAM project (Stream, 2013)) to evaluate relevant ATM KPIs that provide aggregated information about the sensitivity of the system to trajectory interactions, taking into account the system dynamics at a network level. The proposed approach contributes to enhancing the ATM capacity of airports to mitigate network perturbations.     

决策支持物流管理信息系统的设计与开发

介绍了决策支持系统的概念及其内容，分析了在物流管理信息系统基础上实施决策支持系统的必要性和可行性，并针对库存问题研制了决策支持系统，研究了决策支持在物流企业中的实际应用．利用现代的信息分析技术，把决策支持系统应用于信息系统，将管理信息系统延伸为决策支持下的管理系统，为企业决策者提供决策支持，提高企业核心竞争力．     

数据挖掘技术在乳化器故障诊断中的应用

决策树是一个类似于流程图的树结构,主要用途是提取分类规则,进行分类预测。该文针对乳化炸药生产过程中的乳化器维护面临的实际问题,提出了用决策树算法对反映设备状态属性的数据进行挖掘,发现故障数据中存在的规律,并以规则的形式表现出来,从而为乳化器的维护提供决策信息。结果表明,采用决策树方法所得出的规则是正确的,并能正确用于故障的分类工作。     

台风预报现状及其对船舶避台决策的影响

准确的TC预报是避台决策安全、经济的重要保证,但受目前理论和技术水平的限制,完全准确的TC预报无法得到。因此,了解目前气象部门对TC预报的最新进展和动向,准确把握气象部门所能提供的TC预报水平,对于避台决策制定者有着十分重要的意义。我们通过对大量有关TC预报文献的分析研究,针对目前TC预报研究的最新进展情况,重点介绍了当前各国TC移动路径和风浪场的预报水平;提出了所存在的问题及对船舶避台决策的影响,指出进行绕避TC决策所需要解决的重要课题之一,是能够合理地、有效地使用各个不同预报台站的预报结果,科学地提取其中的正确信息,而目前气象部门广泛使用的集成预报方法有可能为此问题提供一个有效的解决办法。     

Markov决策过程在船舶结构维修中的应用

船舶腐蚀和疲劳是导致船体老化,进而使其逐渐丧失结构承载能力的重要原因.本文采用具有无后效性的Markov链来模拟船体结构老化过程,其状态空间由完好(不需要维修)、可见裂纹维修、油漆和阴极防护维修、腐蚀修理、疲劳裂纹修理、腐蚀疲劳组合修理和完全失效共计七种状态组成.每一状态均可采取无修理、修理和更新三者之一的维修方案.通过迭代来获得最佳维修方案以实现单位时间成本最低的维修优化目标.算例表明,Markov链可以合理地模拟船舶结构的老化过程,并可方便快捷地运用于船舶维修经济论证.     

基于ECDIS的舰船机动智能决策支持系统的研究

针对舰船的特点,提出了一种基于ECDIS的舰船机动智能决策支持系统的研究新思路.本系统将决策支持系统与智能专家系统相结合,即充分发挥了专家系统的经验知识,又利用舰船机动的数学模型,做到了定性和定量分析的有机结合.